文章基本信息

标题：Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method
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作者：Posen Lee ; Tai-Been Chen ; Chin-Hsuan Liu 等
期刊名称：Biosensors
电子版ISSN：2079-6374
出版年度：2022
卷号：12
期号：5
DOI：10.3390/bios12050295
语种：English
出版社：MDPI Publishing
摘要：Many neurological and musculoskeletal disorders are associated with problems related to postural movement. Noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. A total of 35 young adults without any health problems were recruited for this study to participate in a walking experiment. An iso-block postural identity method was used to quantitatively analyze posture control and walking behavior. The participants who exhibited straightforward walking and skewed walking were defined as the control and experimental groups, respectively. Fusion deep learning was applied to generate dynamic joint node plots by using OpenPose-based methods, and skewness was qualitatively analyzed using convolutional neural networks. The maximum specificity and sensitivity achieved using a combination of ResNet101 and the naïve Bayes classifier were 0.84 and 0.87, respectively. The proposed approach successfully combines cell phone camera recordings, cloud storage, and fusion deep learning for posture estimation and classification.
关键词：eniso-block postural identityOpenPosefusion deep learning